Merge tag 'v1.0.4' into develop

- (new) Cupy implementation of KPM
- (fix) fix tf version in requirements

Author: devmessias

README.md

@@ -1,12 +1,14 @@
 # ![eMaTe](emate.png)
 
-eMaTe is a python package implemented in tensorflow which the main goal is provide useful methods capable of estimate spectral densities and trace functions of large sparse matrices. 
+eMaTe it is a python package which the main goal is to provide  methods capable of estimating the spectral densities and trace 
+functions of large sparse matrices. eMaTe can run in both CPU and GPU and can estimate the spectral density and related trace functions, such as entropy and Estrada index, even in directed or undirected networks with million of nodes.
 
 ## Install                                                                                                              
 ```
 pip install emate
 ```
 
+If you a have a GPU you should also install cupy.
 ## Kernel Polynomial Method (KPM)
 
 The Kernel Polynomial Method can estimate the spectral density of large sparse Hermitan matrices with a computational cost almost linear. This method combines three key ingredients: the Chebyshev expansion + the stochastic trace estimator + kernel smoothing.
@@ -26,15 +28,15 @@ vals = np.linalg.eigvalsh(W).real
 ```
 
 ```python
-from emate.hermitian import pykpm
+from emate.hermitian import tfkpm
 from stdog.utils.misc import ig2sparse 
 
 W = ig2sparse(G)
 
-num_moments = 300
-num_vecs = 200
+num_moments = 40
+num_vecs = 40
 extra_points = 10
-ek, rho = pykpm(W, num_moments, num_vecs, extra_points)
+ek, rho = tfkpm(W, num_moments, num_vecs, extra_points)
 ```
 
 ```python
@@ -43,6 +45,18 @@ plt.hist(vals, density=True, bins=100, alpha=.9, color="steelblue")
 plt.scatter(ek, rho, c="tomato", zorder=999, alpha=0.9, marker="d")
 
 ```
+If the CUPY package it is available in your machine, you can also use the cupy implementation. When compared to tf-kpm, the
+Cupy-kpm is slower for median matrices (100k) and faster for larger matrices (> 10^6). The main reason it's because the tf-kpm was implemented in order to calc all te moments in a single step. 
+
+```python
+from emate.hermitian import cupykpm
+
+num_moments = 40
+num_vecs = 40
+extra_points = 10
+ek, rho = cupykpm(W, num_moments, num_vecs, extra_points)
+```
+
 
 ![](docs/source/imgs/kpm.png)
 

emate.png

@@ -3,6 +3,12 @@
 
 from emate import linalg
 from emate import utils
+
+try:
+    import cupy as cp
+except:
+    print("Warning: Cupy package not found")
+
 __version__ = "1.0.4"
 __license__ = ""
 __author__ = "Bruno Messias; Thomas K Peron"

emate/__init__.py

@@ -1,4 +1,4 @@
-from emate.hermitian.kpm import pykpm, cupykpm
+from emate.hermitian.kpm import pykpm, cupykpm, tfkpm
 from emate.hermitian import tfops, cupyops
 
-__all__ = ["pykpm", "tfops", "cupykpm", "cupykpm"]
+__all__ = ["pykpm", "tfops", "cupykpm", "cupykpm", "tfkpm"]

emate/hermitian/__init__.py

@@ -20,15 +20,19 @@
 
 """
 import numpy as np
-import cupy as cp
+try:
+    import cupy as cp
+except:
+    cp = None
+
 from emate.utils.cupyops.signal import dctIII
 
 
 def get_moments(
     H_rescaled,
     num_moments,
     dimension,
-    cp_complex=cp.complex64
+    precision=32
 ):
     """
     Parameters
@@ -44,7 +48,10 @@ def get_moments(
     Returns
     -------
     """
-
+    cp_complex = cp.complex64
+    if precision == 64:
+        cp_complex = cp.complex128
+        
     alpha0 = cp.exp(1j*2*cp.pi*cp.random.rand(dimension))
     alpha1 = H_rescaled.dot(alpha0)
     mu = cp.zeros(num_moments, dtype=cp_complex)

emate/hermitian/cupyops/kpm.py

@@ -32,7 +32,10 @@
 """
 import numpy as np
 import tensorflow as tf
-import cupy as cp
+try:
+    import cupy as cp
+except:
+    cp = None
 
 from emate.linalg import rescale_matrix, get_bounds
 from emate.linalg.misc import rescale_cupy
@@ -206,10 +209,10 @@ def cupykpm(
     H, scale_fact_a, scale_fact_b = rescale_cupy(H, lmin, lmax, epsilon)
 
     moments = cp.array([
-        cupyops.get_moments(H, num_moments, dimension)
+        cupyops.get_moments(H, num_moments, dimension, precision=precision)
         for i in range(num_vecs)
     ])
-    kernel0 = cupy_jackson(num_moments, precision=32)
+    kernel0 = cupy_jackson(num_moments, precision=precision)
 
     ek, rho = cupyops.apply_kernel(
         moments,

emate/hermitian/kpm.py

@@ -10,7 +10,10 @@
 """
 import scipy
 import scipy.sparse.linalg
-import cupy as cp
+try:
+    import cupy as cp
+except:
+    cp = None
 
 
 def get_bounds(

emate/linalg/misc.py

@@ -12,7 +12,10 @@
     - jackson
 """
 
-import cupy as cp
+try:
+    import cupy as cp
+except:
+    cp = None
 
 
 def jackson(

emate/utils/cupyops/kernels.py

@@ -11,8 +11,10 @@
         Cosine transform of type III.
 """
 
-
-import cupy as cp
+try:
+    import cupy as cp
+except:
+    cp = None
 
 
 def dctIII(x, precision=32):

emate/utils/cupyops/signal.py

@@ -6,7 +6,7 @@
 
 setup(
     name="emate",
-    version="v1.0.3",
+    version="v1.0.4",
     packages=find_packages(exclude=["build", ]),
     long_description=README_TEXT,
     long_description_content_type="text/markdown",